{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Softmax exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "This exercise is analogous to the SVM exercise. You will:\n",
    "\n",
    "- implement a fully-vectorized **loss function** for the Softmax classifier\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** with numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mada/anaconda3/envs/py27/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3073)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3073)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 3073)\n",
      "Test labels shape:  (1000,)\n",
      "dev data shape:  (500, 3073)\n",
      "dev labels shape:  (500,)\n"
     ]
    }
   ],
   "source": [
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, num_dev=500):\n",
    "  \"\"\"\n",
    "  Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "  it for the linear classifier. These are the same steps as we used for the\n",
    "  SVM, but condensed to a single function.  \n",
    "  \"\"\"\n",
    "  # Load the raw CIFAR-10 data\n",
    "  cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "  X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "  \n",
    "  # subsample the data\n",
    "  mask = range(num_training, num_training + num_validation)\n",
    "  X_val = X_train[mask]\n",
    "  y_val = y_train[mask]\n",
    "  mask = range(num_training)\n",
    "  X_train = X_train[mask]\n",
    "  y_train = y_train[mask]\n",
    "  mask = range(num_test)\n",
    "  X_test = X_test[mask]\n",
    "  y_test = y_test[mask]\n",
    "  mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "  X_dev = X_train[mask]\n",
    "  y_dev = y_train[mask]\n",
    "  \n",
    "  # Preprocessing: reshape the image data into rows\n",
    "  X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "  X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "  X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "  X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "  \n",
    "  # Normalize the data: subtract the mean image\n",
    "  mean_image = np.mean(X_train, axis = 0)\n",
    "  X_train -= mean_image\n",
    "  X_val -= mean_image\n",
    "  X_test -= mean_image\n",
    "  X_dev -= mean_image\n",
    "  \n",
    "  # add bias dimension and transform into columns\n",
    "  X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "  X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "  X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "  X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "  \n",
    "  return X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev = get_CIFAR10_data()\n",
    "print 'Train data shape: ', X_train.shape\n",
    "print 'Train labels shape: ', y_train.shape\n",
    "print 'Validation data shape: ', X_val.shape\n",
    "print 'Validation labels shape: ', y_val.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'Test labels shape: ', y_test.shape\n",
    "print 'dev data shape: ', X_dev.shape\n",
    "print 'dev labels shape: ', y_dev.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Softmax Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/softmax.py**. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 2.386615\n",
      "sanity check: 2.302585\n"
     ]
    }
   ],
   "source": [
    "# First implement the naive softmax loss function with nested loops.\n",
    "# Open the file cs231n/classifiers/softmax.py and implement the\n",
    "# softmax_loss_naive function.\n",
    "\n",
    "from cs231n.classifiers.softmax import softmax_loss_naive\n",
    "import time\n",
    "\n",
    "# Generate a random softmax weight matrix and use it to compute the loss.\n",
    "W = np.random.randn(3073, 10) * 0.0001\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# As a rough sanity check, our loss should be something close to -log(0.1).\n",
    "print 'loss: %f' % loss\n",
    "print 'sanity check: %f' % (-np.log(0.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 1:\n",
    "Why do we expect our loss to be close to -log(0.1)? Explain briefly.**\n",
    "\n",
    "**Your answer:** Because the weights have been initialized with very small values, which means that the scores for each class will be very similar. Score for the correct class will be $y_{pred} = \\frac{e^{WX}}{num_{classes}*e^{WX}}$. Since we have ten classes it will be 0.1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 0.562606 analytic: 0.562606, relative error: 1.130649e-08\n",
      "numerical: 1.184773 analytic: 1.184773, relative error: 2.034835e-08\n",
      "numerical: -0.204748 analytic: -0.204748, relative error: 4.630217e-07\n",
      "numerical: -0.136069 analytic: -0.136069, relative error: 4.208862e-07\n",
      "numerical: 2.122666 analytic: 2.122666, relative error: 1.344713e-08\n",
      "numerical: 2.786050 analytic: 2.786050, relative error: 2.741751e-08\n",
      "numerical: -0.344892 analytic: -0.344892, relative error: 1.959802e-07\n",
      "numerical: -1.382615 analytic: -1.382615, relative error: 3.384165e-09\n",
      "numerical: 0.905819 analytic: 0.905818, relative error: 3.788725e-08\n",
      "numerical: 0.982341 analytic: 0.982340, relative error: 1.033136e-07\n",
      "numerical: -1.156056 analytic: -1.156056, relative error: 1.841018e-09\n",
      "numerical: 3.622852 analytic: 3.622852, relative error: 5.582354e-09\n",
      "numerical: -0.545828 analytic: -0.545828, relative error: 1.728839e-08\n",
      "numerical: 0.995832 analytic: 0.995832, relative error: 1.436639e-08\n",
      "numerical: -0.916519 analytic: -0.916519, relative error: 1.699489e-08\n",
      "numerical: 0.683075 analytic: 0.683075, relative error: 9.615383e-08\n",
      "numerical: 2.151084 analytic: 2.151084, relative error: 6.324870e-08\n",
      "numerical: 1.554672 analytic: 1.554672, relative error: 4.850499e-08\n",
      "numerical: 1.158482 analytic: 1.158482, relative error: 8.392133e-08\n",
      "numerical: 1.382728 analytic: 1.382728, relative error: 6.548602e-09\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of softmax_loss_naive and implement a (naive)\n",
    "# version of the gradient that uses nested loops.\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# As we did for the SVM, use numeric gradient checking as a debugging tool.\n",
    "# The numeric gradient should be close to the analytic gradient.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad, 10)\n",
    "\n",
    "# similar to SVM case, do another gradient check with regularization\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 1e2)\n",
    "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 1e2)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "naive loss: 2.386615e+00 computed in 0.012003s\n",
      "vectorized loss: 2.386615e+00 computed in 0.011198s\n",
      "Loss difference: 0.000000\n",
      "Gradient difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Now that we have a naive implementation of the softmax loss function and its gradient,\n",
    "# implement a vectorized version in softmax_loss_vectorized.\n",
    "# The two versions should compute the same results, but the vectorized version should be\n",
    "# much faster.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = softmax_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'naive loss: %e computed in %fs' % (loss_naive, toc - tic)\n",
    "\n",
    "from cs231n.classifiers.softmax import softmax_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, grad_vectorized = softmax_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic)\n",
    "\n",
    "# As we did for the SVM, we use the Frobenius norm to compare the two versions\n",
    "# of the gradient.\n",
    "grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print 'Loss difference: %f' % np.abs(loss_naive - loss_vectorized)\n",
    "print 'Gradient difference: %f' % grad_difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-07 reg 1.000000e+03 train accuracy: 0.228694 val accuracy: 0.236000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.330184 val accuracy: 0.345000\n",
      "lr 2.800000e-06 reg 1.000000e+03 train accuracy: 0.397490 val accuracy: 0.387000\n",
      "lr 2.800000e-06 reg 5.000000e+04 train accuracy: 0.279592 val accuracy: 0.288000\n",
      "best validation accuracy achieved during cross-validation: 0.387000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of over 0.35 on the validation set.\n",
    "from cs231n.classifiers import Softmax\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_softmax = None\n",
    "learning_rates = [2.8e-6, 1e-7]\n",
    "regularization_strengths = [1e+03, 5e4]\n",
    "\n",
    "grid_search=[(x,y) for x in learning_rates for y in regularization_strengths]\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained softmax classifer in best_softmax.                          #\n",
    "################################################################################\n",
    "for alpha, reg in grid_search:\n",
    "    softmax=Softmax()\n",
    "    softmax.train(X_train, y_train, learning_rate=alpha, reg=reg, num_iters=1000)\n",
    "    y_train_pred=softmax.predict(X_train)\n",
    "    y_val_pred=softmax.predict(X_val)\n",
    "    training_accuracy=np.mean(y_train_pred==y_train)\n",
    "    validation_accuracy=np.mean(y_val_pred==y_val)\n",
    "    \n",
    "    results[alpha, reg] = (training_accuracy, validation_accuracy)\n",
    "    \n",
    "    if validation_accuracy > best_val:\n",
    "        best_val=validation_accuracy\n",
    "        best_softmax=softmax\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print 'lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy)\n",
    "    \n",
    "print 'best validation accuracy achieved during cross-validation: %f' % best_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problems with overflow\n",
    "\n",
    "If you choose different hyperparameters you might get overflow errors. Please visit [this page](https://www.reddit.com/r/cs231n/comments/41noqi/overflow_detected_while_training_linear_svm/) in case this happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "softmax on raw pixels final test set accuracy: 0.385000\n"
     ]
    }
   ],
   "source": [
    "# evaluate on test set\n",
    "# Evaluate the best softmax on test set\n",
    "y_test_pred = best_softmax.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print 'softmax on raw pixels final test set accuracy: %f' % (test_accuracy, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7P0Pf5Tmmbq1Xu2PycCV9SRNrzAlH9zOPRmfcekKpt+ktzAbdPPCgKexlLZbm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TAa/NzZV+DX16H3x/UIK57068yZDu/H2xo9r2tAX7V28WCqlld5XFuwOM/75GMXPYdmpd3P8VPr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\neqERQgghhBBCVCz/BTiDKHQL5HBwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93d8877450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class\n",
    "w = best_softmax.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in xrange(10):\n",
    "  plt.subplot(2, 5, i + 1)\n",
    "  \n",
    "  # Rescale the weights to be between 0 and 255\n",
    "  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "  plt.imshow(wimg.astype('uint8'))\n",
    "  plt.axis('off')\n",
    "  plt.title(classes[i])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [py27]",
   "language": "python",
   "name": "Python [py27]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
